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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the initial to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

Google’s model works by spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the reality that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that although the AI is beating all other models on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the DeepMind output more useful for forecasters by offering additional internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that although these forecasts appear highly accurate, the results of the system is kind of a black box,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a peek into its techniques – in contrast to most other models which are provided free to the public in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to solve difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.